大型语言模型在分析常见高血压情景中的性能
Jaleh Zand1, Jing Miao1, Musab S Hommos2
1Division of Nephrology and Hypertension (J.Z., J.M., G.L.S., S.J.T., W.C., V.D.G., Z.M.Z.), Mayo Clinic, Rochester, MN.
Hypertension (Dallas, Tex. : 1979)
|November 3, 2025
概括
大型语言模型 (LLM) 显示了帮助高血压管理的潜力,GPT-4在测试模型中显示了最高的准确性和安全性. 然而,目前的LLM还没有优于专家建议,在临床应用中需要人类监督.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 心血管疾病管理管理
背景情况:
- 高血压是心血管死亡的主要原因,控制率低于最佳.
- 大型语言模型 (LLM) 通过协助临床决策提供了改善高血压管理的潜力.
- 在指导方针驱动的医疗任务中,LLM的可靠性需要进行彻底的评估.
研究的目的:
- 评估高血压管理建议的准确性和安全性,由三个不同的LLMs产生的.
- 将LLM的表现与专家为高血压护理提出的建议进行比较.
- 确定基于LLM的临床咨询的指导方针一致性和可靠性.
主要方法:
- 为高血压管理场景开发了51个临床细节.
- 答案是由三个LLM (GPT-4,Gemini,MedLM) 和一名高血压专家生成的.
- 盲目的审稿人评估了准确性,安全性和来源识别方面的答案.
主要成果:
- 在LLM中,GPT-4实现了最高的准确度 (83%) 和安全性 (86%),但仍然低于专家的性能 (92%的准确度,93%的安全性).
- 双子 (64%的准确率,73%的安全性) 和MedLM (35%的准确率,39%的安全性) 的表现明显较低.
- 与其他LLM相比,GPT-4的指导方针一致性更高 (46%),但落后于专家建议 (68%).
结论:
- 由于与专家决定更紧密地协调,GPT-4显示出支持高血压管理的前景.
- 目前LLM高血压管理能力低于人类专家的能力.
- 持续的人在循环监督对于在临床环境中安全有效地部署LLM至关重要.
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